[link text](https://)• DOMAIN: Automotive Surveillance. • CONTEXT: Computer vision can be used to automate supervision and generate action appropriate action trigger if the event is predicted from the image of interest. For example a car moving on the road can be easily identified by a camera as make of the car, type, colour, number plates etc. • DATA DESCRIPTION: The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. Data description: ‣ Train Images: Consists of real images of cars as per the make and year of the car. ‣ Test Images: Consists of real images of cars as per the make and year of the car. ‣ Train Annotation: Consists of bounding box region for training images. ‣ Test Annotation: Consists of bounding box region for testing images. Dataset has been attached along with this project. Please use the same for this capstone project. Original link to the dataset for your reference only: https://www.kaggle.com/jutrera/stanford-car-dataset-by-classes-folder [ for your reference only ] Reference: 3D Object Representations for Fine-Grained Categorisation, Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei 4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.